Determine bit error count based on signal and noise characteristics centered at an optimized read voltage

ABSTRACT

A memory device to estimate a bit error count of data retrievable from a group of memory cells. For example, the memory device has a group of memory cells programmed to store a predetermined number of bits per memory cells to be read at a plurality of first voltages. The memory device determines a plurality of calibrated read voltages corresponding to the plurality of first voltages respectively, based on first signal and noise characteristics of the group of memory cells. The first signal and noise characteristics are used to compute second signal and noise characteristics of the group of memory cells for the calibrated read voltages. The second signal and noise characteristics are used in an empirical formula to compute an estimate of the bit error count of data retrievable from the group of memory cells using the calibrated read voltages.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate to memory systems ingeneral, and more particularly, but not limited to memory systemsconfigured to estimate bit error count in data retrieved using anoptimized voltage for reading data from memory cells.

BACKGROUND

A memory sub-system can include one or more memory devices that storedata. The memory devices can be, for example, non-volatile memorydevices and volatile memory devices. In general, a host system canutilize a memory sub-system to store data at the memory devices and toretrieve data from the memory devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates an example computing system having a memorysub-system in accordance with some embodiments of the presentdisclosure.

FIG. 2 illustrates an integrated circuit memory device having acalibration circuit configured to measure signal and noisecharacteristics according to one embodiment.

FIG. 3 shows an example of measuring signal and noise characteristics toimprove memory operations according to one embodiment.

FIGS. 4-6 illustrate a technique to compute an optimized read voltagefrom count differences according to one embodiment.

FIGS. 7 to 10 illustrate a technique to estimate signal and noisecharacteristics centered at an optimized read voltage according to oneembodiment.

FIG. 11 shows a method to calculate signal and noise characteristicscentered at an optimized read voltage for reading a group of memorycells according to one embodiment.

FIG. 12 shows a method to estimate a bit error count in data retrievedusing a read voltage optimized for reading a group of memory cellsaccording to one embodiment.

FIG. 13 is a block diagram of an example computer system in whichembodiments of the present disclosure can operate.

DETAILED DESCRIPTION

At least some aspects of the present disclosure are directed to a memorysub-system configured to measure signal and noise characteristics of agroup of memory cells to compute an optimized voltage for reading thegroup of memory cells, estimate signal and noise characteristicscentered at the optimized read voltage, and use the estimated signal andnoise characteristics to calculate a bit error count in data retrievedfrom the memory cells at the optimized read voltage. Examples of storagedevices and memory modules are described below in conjunction withFIG. 1. In general, a host system can utilize a memory sub-system thatincludes one or more components, such as memory devices that store data.The host system can provide data to be stored at the memory sub-systemand can request data to be retrieved from the memory sub-system.

An integrated circuit memory cell (e.g., a flash memory cell) can beprogrammed to store data by the way of its state at a threshold voltage.For example, if the memory cell is configured/programmed in a state thatallows a substantial current to pass the memory cell at the thresholdvoltage, the memory cell is storing a bit of one; and otherwise, thememory cell is storing a bit of zero. Further, a memory cell can storemultiple bits of data by being configured/programmed differently atmultiple threshold voltages. For example, the memory cell can storemultiple bits of data by having a combination of states at the multiplethreshold voltages; and different combinations of the states of thememory cell at the threshold voltages can be interpreted to representdifferent states of bits of data that is stored in the memory cell.

However, after the states of integrated circuit memory cells areconfigured/programmed using write operations to store data in the memorycells, the optimized threshold voltage for reading the memory cells canshift due to a number of factors, such as charge loss, read disturb,cross-temperature effect (e.g., write and read at different operatingtemperatures), etc., especially when a memory cell is programmed tostore multiple bits of data.

Data can be encoded with redundant information to facilitate errordetection and recovery. When data encoded with redundant information isstored in a memory sub-system, the memory sub-system can detect errorsin raw, encoded data retrieved from the memory sub-system and/or recoverthe original, non-encoded data that is used to generated encoded datafor storing in the memory sub-system. The recovery operation can besuccessful (or have a high probability of success) when the raw, encodeddata retrieved from the memory sub-system contains less than a thresholdamount of errors, or the bit error rate in the encoded data is lowerthan a threshold. For example, error detection and data recovery can beperformed using techniques such as Error Correction Code (ECC),Low-Density Parity-Check (LDPC) code, etc.

When the encoded data retrieved from the memory cells of the memorysub-system has too many errors for successful decoding, the memorysub-system may retry the execution of the read command with adjustedparameters for reading the memory cells. However, it is inefficient tosearch for a set of parameters through multiple read retry with multiplerounds of calibration, reading, decoding failure, and retry, until theencoded data retrieved from the memory cells can be decoded into errorfree data. For example, blind searching for the optimized read voltagesis inefficient. For example, one or more commands being injected betweenretry reads can lead to long latency for recovering data from errors.

Conventional calibration circuitry has been used to self-calibrate amemory region in applying read level signals to account for shift ofthreshold voltages of memory cells within the memory region. During thecalibration, the calibration circuitry is configured to apply differenttest signals to the memory region to count the numbers of memory cellsthat output a specified data state for the test signals. Based on thecounts, the calibration circuitry determines a read level offset valueas a response to a calibration command.

At least some aspects of the present disclosure address the above andother deficiencies by computing a voltage optimized to read a group ofmemory cells from signal and noise characteristics of the group ofmemory cells using an efficient method that can be implemented in amemory device. After the optimized read voltage is computed from themeasured signal and noise characteristics, signal and noisecharacteristics centered at the optimized read voltage can be estimatedfrom the measured signal and noise characteristics (e.g., to estimatebit error rate).

For example, in response to a command from a controller of a memorysub-system, a memory device can automatically calibrate a voltage forreading a group of memory cells based on signal and noisecharacteristics measured for memory cells. The signal and noisecharacteristics measured for memory cells can be based on a bit count ofmemory cells in the group having a predetermined status when a testvoltage is applied to read the memory cells. Different test voltagesthat are separated from one another by a predetermined voltage intervalor gap can have different bit counts. The difference between bit countsof two adjacent test voltages provides the count difference for thevoltage interval or gap between the adjacent test voltages. An optimizedread voltage can be found at a voltage where the distribution of thecount differences over voltage reaches a minimum.

When one of the count differences is smaller than its two adjacentneighbors, a minimum can be determined to be in the voltage interval orgap of the smallest count difference. An improved location of theoptimized read voltage within the gap can be computed based on a ratioof adjacent neighbors, as further discussed below in connection withFIG. 5.

When no count difference is between two higher adjacent neighbors, theoptimized read voltage can be identified as in a voltage interval or gapcorresponding to a count difference that is smaller than two of the nexttwo count differences. An improved location of the optimized readvoltage within the gap can be computed based on a ratio of bit counts atthe test voltages of the two ends of the gap, as further discussed belowin connection with FIG. 6.

After an optimized read voltage is calculated (e.g., using techniquesillustrated in FIGS. 3-6), the memory device can use the optimized readvoltage to read memory cells and obtain hard bit data, and optionallyboost modulating the applied read voltage(s) to adjacent voltages tofurther read the memory cells for soft bit data.

Preferably, the operations of reading the hard bit data and reading thesoft bit data are scheduled together during the execution of the readcommand to minimize the time required to obtain the soft bit data and/orto avoid delay that can be caused by processing a separate read command,or by intervening operations on the memory cells.

Optionally, the signal and noise characteristics measured for memorycells are further used to evaluate the quality of the hard bit dataretrieved using the calibrated read voltage(s). The evaluation can beperformed at least in part concurrently with the reading of the hard bitdata. Based on the evaluated quality of the hard bit data, the memorydevice may selectively read and/or transmit the soft bit data.

The hard bit data retrieved from a group of memory cells using thecalibrated/optimized read voltage can be decoded using an errordetection and data recovery technique, such as Error Correction Code(ECC), Low-Density Parity-Check (LDPC) code, etc. When the error rate inthe hard bit data is high, the soft bit data, retrieved from the memorycell using read voltages with predetermined offsets from thecalibrated/optimized read voltage, can be used to assist the decoding ofthe hard bit data. When the soft bit data is used, the error recoverycapability is improved in decoding the hard bit data.

Optionally, a controller of a memory sub-system can initially send acommand to a memory device to read hard bit data with calibrated readvoltage; and in response to a failure in the decoding of the hard bitdata, the controller can further send a command to the memory device toread the corresponding soft bit data. Such an implementation isefficient when the likelihood of a failure in decoding the hard bit datawithout soft bit data is lower than a threshold. However, when thelikelihood is above the threshold, the overhead of sending the separatecommand becomes disadvantageous.

When the likelihood of using soft bit data is above a threshold, it isadvantageous to transmit a single command to the memory device to causethe memory device to read the soft bit data and the hard bit datatogether. Further, the memory device can use the signal and noisecharacteristics of the memory cells to predict whether the soft bit datais likely to be used by the controller. If the likelihood of using ofthe soft bit data is lower than a threshold, the memory device can skipreading the soft bit data.

For example, during the calibration operation, the memory device canmeasure the signal and noise characteristics of the memory cells and usethe measurements to calculate an optimized/calibrated read voltage forreading the memory cells. Once the optimized/calibrated read voltage isobtained, the memory device reads the memory cells to obtain the hardbit data. Subsequently, the memory device adjusts the already appliedoptimized/calibrated read voltage (e.g., through boosted modulation) toa predetermined offset (e.g., 50 mV) below the optimized/calibrated readvoltage to retrieve a set of data, and further adjusts the currentlyapplied voltage (e.g., through boosted modulation) to the predeterminedoffset above the optimized/calibrated read voltage to retrieve anotherset of data. The logic operation of XOR (exclusive or) of the two setsof data at the both sides of the offset (e.g., 50 mV) from theoptimized/calibrated read voltage provides the indication of whether thememory cells provide the same reading at the offset locations around theoptimized/calibrated read voltage. The result of the XOR operation canbe used as soft bit data for decoding the hard bit data read using theoptimized/calibrated read voltage. In some implementations, a largeroffset (e.g., 90 mV) can be used to read another set of soft bit datathat indicates whether the memory cells provide the same reading at thelocations according to the larger offset (e.g., 90 mV) around theoptimized/calibrated read voltage.

For example, in response to a read command from a controller of thememory sub-system, a memory device of the memory sub-system performs anoperation to calibrate a read voltage of memory cells. The calibrationis performed by measuring signal and noise characteristics throughreading the memory cells at a number of voltage levels that are near anestimated location of the optimized read voltage. An optimized readvoltage can be calculated based on statistical data of the resultsgenerated from reading the memory cells at the voltage levels. Forexample, the statistical data can include and/or can be based on countsmeasured by calibration circuitry at the voltage levels. Optionally,such signal and noise characteristics can be measured for sub-regions inparallel to reduce the total time for measuring the signal and noisecharacteristics. The statistical data of the results generated fromreading the memory cells at the voltage levels can be used to predictwhether the decoding of the hard bit data retrieved using the optimizedread voltage is likely to require the use of soft bit data forsuccessful decoding. Thus, the transmission of the soft bit data can beperformed selectively based on the prediction.

For example, a predictive model can be generated through machinelearning to estimate or evaluate the quality of data that can beretrieved from a set of memory cells using the calibrated/optimized readvoltage(s). The predictive model can use features calculated from themeasured signal and noise characteristics of the memory cells as inputto generate a prediction. The reading and/or transmission of the softbit data can be selectively skipped based on the prediction.

The quality of data (e.g., the error rate of the hard bit data) readusing the optimized read voltage can be estimated using signal and noisecharacteristics of the group of memory cells. Preferably, the signal andnoise characteristics used to estimate the error rate of the hard bitdata is based on test voltages that are centered on the optimized readvoltage. For example, a count difference between bit counts at two testvoltages that have a center at the optimized read voltage can be usefulin classifying the error rate of the hard bit data read using theoptimized read voltage. In general, the test voltages used to measurethe signal and noise characteristics for computing the optimized readvoltage are not centered at the computed optimized read voltage.However, a simplified technique can be used to estimate signal and noisecharacteristics that would be measured at test voltages arrangedaccording to the optimized read voltage, as further discussed inconnection with FIGS. 7-11.

In some embodiments, the bit error count is estimated using an empiricalformula that is a function of the signal and noise characteristics ofthe group of memory cells centered at the optimized read voltage. Forexample, when the memory cells in the group are programmed to each storemultiple bits of data, the memory cells are read at a plurality of readvoltages to determine the multiple bits stored in each memory cells.Each of the read voltages can be calibrated or optimized via measuringsignal and noise characteristics of the group of memory cells at testvoltages near the respective read voltages. For the plurality ofcalibrated/optimized read voltages, the signal and noise characteristicscentered at the respective calibrated/optimized read voltages can beestimated without reading the group of memory cells again at testvoltages centered at the respective calibrated/optimized read voltages.The estimated signal and noise characteristics centered at therespective calibrated/optimized read voltages can be used in anempirical formula to calculate the bit error count estimated.

For example, an estimated signal and noise characteristics centered ateach respective calibrated/optimized read voltage can be a countdifference between bit counts at two test voltages that are centered atthe respective calibrated/optimized read voltage. Such a countdifference can be estimated from the count differences measured tocalculate the respective calibrated/optimized read voltage. A sum ofsuch count differences estimated for the plurality ofcalibrated/optimized read voltages can be scaled by an empirical factorto obtain the bit error count in data retrieved from the group of memorycells. The empirical factor can be a function of the sum. For example,in one embodiment, the empirical factor is a linear function of the sum;and the coefficients in the linear function can be obtained by curvefitting measured bit error count in similar memory devices.

FIG. 1 illustrates an example computing system 100 that includes amemory sub-system 110 in accordance with some embodiments of the presentdisclosure. The memory sub-system 110 can include media, such as one ormore volatile memory devices (e.g., memory device 140), one or morenon-volatile memory devices (e.g., memory device 130), or a combinationof such.

A memory sub-system 110 can be a storage device, a memory module, or ahybrid of a storage device and memory module. Examples of a storagedevice include a solid-state drive (SSD), a flash drive, a universalserial bus (USB) flash drive, an embedded Multi-Media Controller (eMMC)drive, a Universal Flash Storage (UFS) drive, a secure digital (SD)card, and a hard disk drive (HDD). Examples of memory modules include adual in-line memory module (DIMM), a small outline DIMM (SO-DIMM), andvarious types of non-volatile dual in-line memory module (NVDIMM).

The computing system 100 can be a computing device such as a desktopcomputer, laptop computer, network server, mobile device, a vehicle(e.g., airplane, drone, train, automobile, or other conveyance),Internet of Things (IoT) enabled device, embedded computer (e.g., oneincluded in a vehicle, industrial equipment, or a networked commercialdevice), or such computing device that includes memory and a processingdevice.

The computing system 100 can include a host system 120 that is coupledto one or more memory sub-systems 110. FIG. 1 illustrates one example ofa host system 120 coupled to one memory sub-system 110. As used herein,“coupled to” or “coupled with” generally refers to a connection betweencomponents, which can be an indirect communicative connection or directcommunicative connection (e.g., without intervening components), whetherwired or wireless, including connections such as electrical, optical,magnetic, etc.

The host system 120 can include a processor chipset (e.g., processingdevice 118) and a software stack executed by the processor chipset. Theprocessor chipset can include one or more cores, one or more caches, amemory controller (e.g., controller 116) (e.g., NVDIMM controller), anda storage protocol controller (e.g., PCIe controller, SATA controller).The host system 120 uses the memory sub-system 110, for example, towrite data to the memory sub-system 110 and read data from the memorysub-system 110.

The host system 120 can be coupled to the memory sub-system 110 via aphysical host interface. Examples of a physical host interface include,but are not limited to, a serial advanced technology attachment (SATA)interface, a peripheral component interconnect express (PCIe) interface,universal serial bus (USB) interface, Fibre Channel, Serial AttachedSCSI (SAS), a double data rate (DDR) memory bus, Small Computer SystemInterface (SCSI), a dual in-line memory module (DIMM) interface (e.g.,DIMM socket interface that supports Double Data Rate (DDR)), Open NANDFlash Interface (ONFI), Double Data Rate (DDR), Low Power Double DataRate (LPDDR), or any other interface. The physical host interface can beused to transmit data between the host system 120 and the memorysub-system 110. The host system 120 can further utilize an NVM Express(NVMe) interface to access components (e.g., memory devices 130) whenthe memory sub-system 110 is coupled with the host system 120 by thePCIe interface. The physical host interface can provide an interface forpassing control, address, data, and other signals between the memorysub-system 110 and the host system 120. FIG. 1 illustrates a memorysub-system 110 as an example. In general, the host system 120 can accessmultiple memory sub-systems via a same communication connection,multiple separate communication connections, and/or a combination ofcommunication connections.

The processing device 118 of the host system 120 can be, for example, amicroprocessor, a central processing unit (CPU), a processing core of aprocessor, an execution unit, etc. In some instances, the controller 116can be referred to as a memory controller, a memory management unit,and/or an initiator. In one example, the controller 116 controls thecommunications over a bus coupled between the host system 120 and thememory sub-system 110. In general, the controller 116 can send commandsor requests to the memory sub-system 110 for desired access to memorydevices 130,140. The controller 116 can further include interfacecircuitry to communicate with the memory sub-system 110. The interfacecircuitry can convert responses received from memory sub-system 110 intoinformation for the host system 120.

The controller 116 of the host system 120 can communicate withcontroller 115 of the memory sub-system 110 to perform operations suchas reading data, writing data, or erasing data at the memory devices130,140 and other such operations. In some instances, the controller 116is integrated within the same package of the processing device 118. Inother instances, the controller 116 is separate from the package of theprocessing device 118. The controller 116 and/or the processing device118 can include hardware such as one or more integrated circuits (ICs)and/or discrete components, a buffer memory, a cache memory, or acombination thereof. The controller 116 and/or the processing device 118can be a microcontroller, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), etc.), or another suitable processor.

The memory devices 130, 140 can include any combination of the differenttypes of non-volatile memory components and/or volatile memorycomponents. The volatile memory devices (e.g., memory device 140) canbe, but are not limited to, random access memory (RAM), such as dynamicrandom access memory (DRAM) and synchronous dynamic random access memory(SDRAM).

Some examples of non-volatile memory components include a negative-and(or, NOT AND) (NAND) type flash memory and write-in-place memory, suchas three-dimensional cross-point (“3D cross-point”) memory. Across-point array of non-volatile memory can perform bit storage basedon a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. Additionally, in contrast to manyflash-based memories, cross-point non-volatile memory can perform awrite in-place operation, where a non-volatile memory cell can beprogrammed without the non-volatile memory cell being previously erased.NAND type flash memory includes, for example, two-dimensional NAND (2DNAND) and three-dimensional NAND (3D NAND).

Each of the memory devices 130 can include one or more arrays of memorycells. One type of memory cell, for example, single level cells (SLC)can store one bit per cell. Other types of memory cells, such asmulti-level cells (MLCs), triple level cells (TLCs), quad-level cells(QLCs), and penta-level cells (PLC) can store multiple bits per cell. Insome embodiments, each of the memory devices 130 can include one or morearrays of memory cells such as SLCs, MLCs, TLCs, QLCs, or anycombination of such. In some embodiments, a particular memory device caninclude an SLC portion, and an MLC portion, a TLC portion, or a QLCportion of memory cells. The memory cells of the memory devices 130 canbe grouped as pages that can refer to a logical unit of the memorydevice used to store data. With some types of memory (e.g., NAND), pagescan be grouped to form blocks.

Although non-volatile memory devices such as 3D cross-point type andNAND type memory (e.g., 2D NAND, 3D NAND) are described, the memorydevice 130 can be based on any other type of non-volatile memory, suchas read-only memory (ROM), phase change memory (PCM), self-selectingmemory, other chalcogenide based memories, ferroelectric transistorrandom-access memory (FeTRAM), ferroelectric random access memory(FeRAM), magneto random access memory (MRAM), Spin Transfer Torque(STT)-MRAM, conductive bridging RAM (CBRAM), resistive random accessmemory (RRAM), oxide based RRAM (OxRAM), negative-or (NOR) flash memory,and electrically erasable programmable read-only memory (EEPROM).

A memory sub-system controller 115 (or controller 115 for simplicity)can communicate with the memory devices 130 to perform operations suchas reading data, writing data, or erasing data at the memory devices 130and other such operations (e.g., in response to commands scheduled on acommand bus by controller 116). The controller 115 can include hardwaresuch as one or more integrated circuits (ICs) and/or discretecomponents, a buffer memory, or a combination thereof. The hardware caninclude digital circuitry with dedicated (i.e., hard-coded) logic toperform the operations described herein. The controller 115 can be amicrocontroller, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), etc.), or another suitable processor.

The controller 115 can include a processing device 117 (processor)configured to execute instructions stored in a local memory 119. In theillustrated example, the local memory 119 of the controller 115 includesan embedded memory configured to store instructions for performingvarious processes, operations, logic flows, and routines that controloperation of the memory sub-system 110, including handlingcommunications between the memory sub-system 110 and the host system120.

In some embodiments, the local memory 119 can include memory registersstoring memory pointers, fetched data, etc. The local memory 119 canalso include read-only memory (ROM) for storing micro-code. While theexample memory sub-system 110 in FIG. 1 has been illustrated asincluding the controller 115, in another embodiment of the presentdisclosure, a memory sub-system 110 does not include a controller 115,and can instead rely upon external control (e.g., provided by anexternal host, or by a processor or controller separate from the memorysub-system).

In general, the controller 115 can receive commands or operations fromthe host system 120 and can convert the commands or operations intoinstructions or appropriate commands to achieve the desired access tothe memory devices 130. The controller 115 can be responsible for otheroperations such as wear leveling operations, garbage collectionoperations, error detection and error-correcting code (ECC) operations,encryption operations, caching operations, and address translationsbetween a logical address (e.g., logical block address (LBA), namespace)and a physical address (e.g., physical block address) that areassociated with the memory devices 130. The controller 115 can furtherinclude host interface circuitry to communicate with the host system 120via the physical host interface. The host interface circuitry canconvert the commands received from the host system into commandinstructions to access the memory devices 130 as well as convertresponses associated with the memory devices 130 into information forthe host system 120.

The memory sub-system 110 can also include additional circuitry orcomponents that are not illustrated. In some embodiments, the memorysub-system 110 can include a cache or buffer (e.g., DRAM) and addresscircuitry (e.g., a row decoder and a column decoder) that can receive anaddress from the controller 115 and decode the address to access thememory devices 130.

In some embodiments, the memory devices 130 include local mediacontrollers 150 that operate in conjunction with memory sub-systemcontroller 115 to execute operations on one or more memory cells of thememory devices 130. An external controller (e.g., memory sub-systemcontroller 115) can externally manage the memory device 130 (e.g.,perform media management operations on the memory device 130). In someembodiments, a memory device 130 is a managed memory device, which is araw memory device combined with a local controller (e.g., localcontroller 150) for media management within the same memory devicepackage. An example of a managed memory device is a managed NAND (MNAND)device.

The controller 115 and/or a memory device 130 can include a read manager113 configured to calculate, based on signal and noise characteristicsof a group of memory cells, a voltage optimized for reading the group ofmemory cells, and then calculate an estimate of the bit error count indata read using the optimized voltage based on the signal and noisecharacteristics of the group of memory cells centered at the optimizedvoltage. In some embodiments, the controller 115 in the memorysub-system 110 includes at least a portion of the read manager 113. Inother embodiments, or in combination, the controller 116 and/or theprocessing device 118 in the host system 120 includes at least a portionof the read manager 113. For example, the controller 115, the controller116, and/or the processing device 118 can include logic circuitryimplementing the read manager 113. For example, the controller 115, orthe processing device 118 (processor) of the host system 120, can beconfigured to execute instructions stored in memory for performing theoperations of the read manager 113 described herein. In someembodiments, the read manager 113 is implemented in an integratedcircuit chip disposed in the memory sub-system 110. In otherembodiments, the read manager 113 can be part of firmware of the memorysub-system 110, an operating system of the host system 120, a devicedriver, or an application, or any combination therein.

For example, the read manager 113 implemented in the controller 115 cantransmit a read command or a calibration command to the memory device130. In response to such a command, the read manager 113 implemented inthe memory device 130 is configured to measure signal and noisecharacteristics of a group of memory cells by reading the group ofmemory cells at a plurality of test voltages configured near anestimated location of the optimized read voltage for the group of memorycells. The test voltages can be configured to be equally spaced by asame amount of voltage gap. From a result of reading the group of memorycells at a test voltage, a bit count of memory cells in the group aredetermined to be storing or reporting a predetermined bit (e.g., 0 or 1corresponding to memory cells being conductive or non-conductive at thetest voltage) when the group is read at the test voltage. A countdifference can be computed from the bit counts of each pair of adjacenttest voltages. The read manager 113 compares the count difference toidentify a voltage interval that contains an optimized read voltage andthen estimates a location in the voltage interval for the optimized readvoltage based on comparing the bit counts or count differences that areclosest to the voltage interval. The estimated location can be used asthe optimized read voltage to read hard bit data; and voltages havingpredetermined offsets from the optimized read voltage can be used toread soft bit data. Signal and noise characteristics of the group ofmemory cells that are defined based on test voltages centered at theestimated location can be computed as part of the process of calculatingthe estimated location without reading the group of memory cells at testvoltages configured to be centered at the estimated location of theoptimized read voltage. Such signal and noise characteristics of thegroup of memory cells centered at the optimized read voltage can be usedin an empirical formula to calculate an estimate of bit error count inthe hard bit data.

FIG. 2 illustrates an integrated circuit memory device 130 having acalibration circuit 145 configured to measure signal and noisecharacteristics according to one embodiment. For example, the memorydevices 130 in the memory sub-system 110 of FIG. 1 can be implementedusing the integrated circuit memory device 130 of FIG. 2.

The integrated circuit memory device 130 can be enclosed in a singleintegrated circuit package. The integrated circuit memory device 130includes multiple groups 131, . . . , 133 of memory cells that can beformed in one or more integrated circuit dies. A typical memory cell ina group 131, . . . , 133 can be programmed to store one or more bits ofdata.

Some of the memory cells in the integrated circuit memory device 130 canbe configured to be operated together for a particular type ofoperations. For example, memory cells on an integrated circuit die canbe organized in planes, blocks, and pages. A plane contains multipleblocks; a block contains multiple pages; and a page can have multiplestrings of memory cells. For example, an integrated circuit die can bethe smallest unit that can independently execute commands or reportstatus; identical, concurrent operations can be executed in parallel onmultiple planes in an integrated circuit die; a block can be thesmallest unit to perform an erase operation; and a page can be thesmallest unit to perform a data program operation (to write data intomemory cells). Each string has its memory cells connected to a commonbitline; and the control gates of the memory cells at the same positionsin the strings in a block or page are connected to a common wordline.Control signals can be applied to wordlines and bitlines to address theindividual memory cells.

The integrated circuit memory device 130 has a communication interface147 to receive a command having an address 135 from the controller 115of a memory sub-system 110, retrieve both hard bit data 177 and soft bitdata 173 from the memory address 135, and provide at least the hard bitdata 177 as a response to the command. An address decoder 141 of theintegrated circuit memory device 130 converts the address 135 intocontrol signals to select a group of memory cells in the integratedcircuit memory device 130; and a read/write circuit 143 of theintegrated circuit memory device 130 performs operations to determinethe hard bit data 177 and the soft bit data 173 of memory cells at theaddress 135.

The integrated circuit memory device 130 has a calibration circuit 145configured to determine measurements of signal and noise characteristics139 of memory cells in a group (e.g., 131, . . . , or 133). For example,the statistics of memory cells in a group or region that has aparticular state at one or more test voltages can be measured todetermine the signal and noise characteristics 139. Optionally, thesignal and noise characteristics 139 can be provided by the memorydevice 130 to the controller 115 of a memory sub-system 110 via thecommunication interface 147.

In at least some embodiments, the calibration circuit 145 determines theoptimized read voltage(s) of the group of memory cells based on thesignal and noise characteristics 139. In some embodiments, the signaland noise characteristics 139 are further used in the calibrationcircuit 145 to determine whether the error rate in the hard bit data 177is sufficiently high such that it is preferred to decode the hard bitdata 177 in combination with the soft bit data 173 using a sophisticateddecoder. When the use of the soft bit data 173 is predicted, based onthe prediction/classification of the error rate in the hard bit data177, the read manager 113 can transmit both the soft bit data 173 andthe hard bit data 177 to the controller 115 of the memory sub-system110.

For example, the calibration circuit 145 can measure the signal andnoise characteristics 139 by reading different responses from the memorycells in a group (e.g., 131, . . . , 133) by varying operatingparameters used to read the memory cells, such as the voltage(s) appliedduring an operation to read data from memory cells.

For example, the calibration circuit 145 can measure the signal andnoise characteristics 139 on the fly when executing a command to readthe hard bit data 177 and the soft bit data 173 from the address 135.Since the signal and noise characteristics 139 is measured as part ofthe operation to read the hard bit data 177 from the address 135, thesignal and noise characteristics 139 can be used in the read manager 113with reduced or no penalty on the latency in the execution of thecommand to read the hard bit data 177 from the address 135.

The read manager 113 of the memory device 130 is configured to use thesignal and noise characteristics 139 to determine the voltages used toread memory cells identified by the address 135 for both hard bit dataand soft bit data and to determine whether to transmit the soft bit datato the memory sub-system controller 115.

For example, the read manager 113 can use a predictive model, trainedvia machine learning, to predict the likelihood of the hard bit data 177retrieved from a group of memory cells (e.g., 131 or 133) failing a testof data integrity. The prediction can be made based on the signal andnoise characteristics 139. Before the test is made usingerror-correcting code (ECC) and/or low-density parity-check (LDPC) code,or even before the hard bit data 177 is transferred to a decoder, theread manager 113 uses the signal and noise characteristics 139 topredict the result of the test. Based on the predicted result of thetest, the read manager 113 determines whether to transmit the soft bitdata to the memory sub-system controller 115 in a response to thecommand.

For example, if the hard bit data 177 is predicted to decode using alow-power decoder that uses hard bit data 177 without using the soft bitdata 173, the read manager 113 can skip the transmission of the soft bitdata 173 to the memory sub-system controller 115; and the read manager113 provides the hard bit data 177, read from the memory cells usingoptimized read voltages calculated from the signal and noisecharacteristics 139, for decoding by the low-power decoder. For example,the low-power decoder can be implemented in the memory sub-systemcontroller 115. Alternatively, the low-power decoder can be implementedin the memory device 130; and the read manager 113 can provide theresult of the lower-power decoder to the memory sub-system controller115 as the response to the received command.

For example, if the hard bit data 177 is predicted to fail in decodingin the low-power decoder but can be decoded using a high-power decoderthat uses both hard bit data and soft bit data, the read manager 113 candecide to provide both the hard bit data 177 and the soft bit data 173for decoding by the high-power decoder. For example, the high-powerdecoder can be implemented in the controller 115. Alternatively, thehigh-power decoder can be implemented in the memory device 130.

Optionally, if the hard bit data 177 is predicted to fail in decoding indecoders available in the memory sub-system 110, the read manager 113can decide to skip transmitting the hard bit data 177 to the memorysub-system controller 115, initiate a read retry immediately, such thatwhen the memory sub-system controller 115 requests a read retry, atleast a portion of the read retry operations is performed to reduce thetime for responding to the request from the memory sub-system controller115 for a read retry. For example, during the read retry, the readmanager 113 instructs the calibration circuit 145 to perform a modifiedcalibration to obtain a new set of signal and noise characteristics 139,which can be further used to determine improved read voltages.

The data from the memory cells identified by the address (135) caninclude hard bit data 177 and soft bit data 173. The hard bit data 177is retrieved using optimized read voltages. The hard bit data 177identifies the states of the memory cells that are programmed to storedata and subsequently detected in view of changes caused by factors,such as charge loss, read disturb, cross-temperature effect (e.g., writeand read at different operating temperatures), etc. The soft bit data173 is obtained by reading the memory cells using read voltages centeredat each optimized read voltage with a predetermined offset from thecenter, optimized read voltage. The XOR of the read results at the readvoltages having the offset indicates whether the memory cells providedifferent read results at the read voltages having the offset. The softbit data 173 can include the XOR results. In some instances, one set ofXOR results is obtained based on a smaller offset; and another set ofXOR results is obtained based on a larger offset. In general, multiplesets of XOR results can be obtained for multiple offsets, where eachrespective offset is used to determine a lower read voltage and a higherread voltage such that both the lower and higher read voltages have thesame respective offset from an optimized read voltage to determine theXOR results.

FIG. 3 shows an example of measuring signal and noise characteristics139 to improve memory operations according to one embodiment.

In FIG. 3, the calibration circuit 145 applies different read voltagesV_(A), V_(B), V_(C), V_(D), and V_(E) to read the states of memory cellsin a group (e.g., 131, . . . , or 133). In general, more or less readvoltages can be used to generate the signal and noise characteristics139.

As a result of the different voltages applied during the read operation,a same memory cell in the group (e.g., 131, . . . , or 133) may showdifferent states. Thus, the counts C_(A), C_(B), C_(C), C_(D), and C_(E)of memory cells having a predetermined state at different read voltagesV_(A), V_(B), V_(C), V_(D), and V_(E) can be different in general. Thepredetermined state can be a state of having substantial current passingthrough the memory cells, or a state of having no substantial currentpassing through the memory cells. The counts C_(A), C_(B), C_(C), C_(D),and C_(E) can be referred to as bit counts.

The calibration circuit 145 can measure the bit counts by applying theread voltages V_(A), V_(B), V_(C), V_(D), and V_(E) one at a time on thegroup (e.g., 131, . . . , or 133) of memory cells.

Alternatively, the group (e.g., 131, . . . , or 133) of memory cells canbe configured as multiple subgroups; and the calibration circuit 145 canmeasure the bit counts of the subgroups in parallel by applying the readvoltages V_(A), V_(B), V_(C), V_(D), and V_(E). The bit counts of thesubgroups are considered as representative of the bit counts in theentire group (e.g., 131, . . . , or 133). Thus, the time duration ofobtaining the counts C_(A), C_(B), C_(C), C_(D), and C_(E) can bereduced.

In some embodiments, the bit counts C_(A), C_(B), C_(C), C_(D), andC_(E) are measured during the execution of a command to read the datafrom the address 135 that is mapped to one or more memory cells in thegroup (e.g., 131, . . . , or 133). Thus, the controller 115 does notneed to send a separate command to request for the signal and noisecharacteristics 139 that is based on the bit counts C_(A), C_(B), C_(C),C_(D), and C_(E).

The differences between the bit counts of the adjacent voltages areindicative of the errors in reading the states of the memory cells inthe group (e.g., 133, . . . , or 133).

For example, the count difference D_(A) is calculated from C_(A)−C_(B),which is an indication of read threshold error introduced by changingthe read voltage from V_(A) to V_(B).

Similarly, D_(B)=C_(B)−C_(C); D_(C)=C_(C)−C_(D); and D_(D)=C_(D)−C_(E).

The curve 157, obtained based on the count differences D_(A), D_(B),D_(C), and D_(D), represents the prediction of read threshold error E asa function of the read voltage. From the curve 157 (and/or the countdifferences), the optimized read voltage V_(O) can be calculated as thepoint 153 that provides the lowest read threshold error D_(MIN) on thecurve 157.

In one embodiment, the calibration circuit 145 computes the optimizedread voltage V_(O) and causes the read/write circuit 143 to read thedata from the address 135 using the optimized read voltage V_(O).

Alternatively, the calibration circuit 145 can provide, via thecommunication interface 147 to the controller 115 of the memorysub-system 110, the count differences D_(A), D_(B), D_(C), and D_(D)and/or the optimized read voltage V_(O) calculated by the calibrationcircuit 145.

FIG. 3 illustrates an example of generating a set of statistical data(e.g., bit counts and/or count differences) for reading at an optimizedread voltage V_(O). In general, a group of memory cells can beconfigured to store more than one bit in a memory cell; and multipleread voltages are used to read the data stored in the memory cells. Aset of statistical data can be similarly measured for each of the readvoltages to identify the corresponding optimized read voltage, where thetest voltages in each set of statistical data are configured in thevicinity of the expected location of the corresponding optimized readvoltage. Thus, the signal and noise characteristics 139 measured for amemory cell group (e.g., 131 or 133) can include multiple sets ofstatistical data measured for the multiple threshold voltagesrespectively.

For example, the controller 115 can instruct the memory device 130 toperform a read operation by providing an address 135 and at least oneread control parameter. For example, the read control parameter can be asuggested read voltage.

The memory device 130 can perform the read operation by determining thestates of memory cells at the address 135 at a read voltage and providethe data according to the determined states.

During the read operation, the calibration circuit 145 of the memorydevice 130 generates the signal and noise characteristics 139. The dataand the signal and noise characteristics 139 are provided from thememory device 130 to the controller 115 as a response. Alternatively,the processing of the signal and noise characteristics 139 can beperformed at least in part using logic circuitry configured in thememory device 130. For example, the processing of the signal and noisecharacteristics 139 can be implemented partially or entirely using theprocessing logic configured in the memory device 130. For example, theprocessing logic can be implemented using Complementarymetal-oxide-semiconductor (CMOS) circuitry formed under the array ofmemory cells on an integrated circuit die of the memory device 130. Forexample, the processing logic can be formed, within the integratedcircuit package of the memory device 130, on a separate integratedcircuit die that is connected to the integrated circuit die having thememory cells using Through-Silicon Vias (TSVs) and/or other connectiontechniques.

The signal and noise characteristics 139 can be determined based atleast in part on the read control parameter. For example, when the readcontrol parameter is a suggested read voltage for reading the memorycells at the address 135, the calibration circuit 145 can compute theread voltages V_(A), V_(B), V_(C), V_(D), and V_(E) that are in thevicinity of the suggested read voltage.

The signal and noise characteristics 139 can include the bit countsC_(A), C_(B), C_(C), C_(D), and C_(E). Alternatively, or in combination,the signal and noise characteristics 139 can include the countdifferences D_(A), D_(B), D_(C), and D_(D).

Optionally, the calibration circuit 145 uses one method to compute anoptimized read voltage V_(O) from the count differences D_(A), D_(B),D_(C), and D_(D); and the controller 115 uses another different methodto compute the optimized read voltage V_(O) from the signal and noisecharacteristics 139 and optionally other data that is not available tothe calibration circuit 145.

When the calibration circuit 145 can compute the optimized read voltageV_(O) from the count differences D_(A), D_(B), D_(C), and D_(D)generated during the read operation, the signal and noisecharacteristics can optionally include the optimized read voltage V_(O).Further, the memory device 130 can use the optimized read voltage V_(O)in determining the hard bit data in the data from the memory cells atthe address 135. The soft bit data in the data can be obtained byreading the memory cells with read voltages that are a predeterminedoffset away from the optimized read voltage V_(O). Alternatively, thememory device 130 uses the controller-specified read voltage provided inthe read control parameter in reading the data.

The controller 115 can be configured with more processing power than thecalibration circuit 145 of the integrated circuit memory device 130.Further, the controller 115 can have other signal and noisecharacteristics applicable to the memory cells in the group (e.g., 133,. . . , or 133). Thus, in general, the controller 115 can compute a moreaccurate estimation of the optimized read voltage V_(O) (e.g., for asubsequent read operation, or for a retry of the read operation).

In general, it is not necessary for the calibration circuit 145 toprovide the signal and noise characteristics 139 in the form of adistribution of bit counts over a set of read voltages, or in the formof a distribution of count differences over a set of read voltages. Forexample, the calibration circuit 145 can provide the optimized readvoltage V_(O) calculated by the calibration circuit 145, as signal andnoise characteristics 139.

The calibration circuit 145 can be configured to generate the signal andnoise characteristics 139 (e.g., the bit counts, or bit countdifferences) as a byproduct of a read operation. The generation of thesignal and noise characteristics 139 can be implemented in theintegrated circuit memory device 130 with little or no impact on thelatency of the read operation in comparison with a typical read withoutthe generation of the signal and noise characteristics 139. Thus, thecalibration circuit 145 can determine signal and noise characteristics139 efficiently as a byproduct of performing a read operation accordingto a command from the controller 115 of the memory sub-system 110.

In general, the calculation of the optimized read voltage V_(O) can beperformed within the memory device 130, or by a controller 115 of thememory sub-system 110 that receives the signal and noise characteristics139 as part of enriched status response from the memory device 130.

The hard bit data 177 can be obtained by applying the optimized readvoltage V_(O) on the group of memory cells and determining the state ofthe memory cells while the memory cells are subjected to the optimizedread voltages V_(O).

The soft bit data 173 can be obtained by applying the read voltages 181and 182 that are offset from the optimized read voltage V_(O) with apredetermined amount. For example, the read voltage 181 is at the offset183 of the predetermined amount lower from the optimized read voltageV_(O); and the read voltage 182 is at the offset 184 of the samepredetermined amount higher from the optimized read voltage V_(O). Amemory cell subjected to the read voltage 181 can have a state that isdifferent from the memory cell subjected to the read voltage 182. Thesoft bit data 173 can include or indicate the XOR result of the dataread from the memory cell using the read voltages 181 and 182. The XORresult shows whether the memory cell subjected to the read voltage 181has the same state as being to the read voltage 182.

FIGS. 4-6 illustrate a technique to compute an optimized read voltagefrom count differences according to one embodiment. The technique ofFIGS. 4-6 simplifies the computation for calculating the optimized readvoltage V_(O) such that the computation can be implemented using reducedcomputing power and/or circuitry.

The computation illustrated in FIGS. 4-6 can be performed based on thebit counts and count differences illustrated in FIG. 3 for test voltagesV_(A), V_(B), V_(C), V_(D), and V_(E).

In FIG. 4, an operation 201 is performed to compare the two center countdifferences D_(B) and D_(C).

If D_(B) is greater than D_(C), it can be assumed that a minimal can befound on the higher half of the test voltage region between V_(C) toV_(E). Thus, operation 203 is performed to compare the lower one D_(C)of the two center bit count differences with its other neighbor D_(D).

If D_(C) is no greater than its other neighbor D_(D), D_(C) is nogreater than its neighbors D_(B) and D_(D). Thus, it can be inferredthat a minimal can be found between the test voltages V_(C) and V_(D).Based on a ratio between the differences of D_(C) from its neighborsD_(B) and D_(D), an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique similar to thatillustrated in FIG. 5.

If D_(C) is greater than its other neighbor D_(D), it can be assumedthat a minimal can be in the highest test voltage interval between V_(D)and V_(E). Thus, an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique similar to thatillustrated in FIG. 6, based on a ratio of count differences D_(D) andD_(C) that are closest to the test voltages V_(D) and V_(E).

Similarly, if D_(B) is no greater than D_(C), it can be assumed that aminimal can be found on the lower half of the test voltage regionbetween V_(A) to V_(C). Thus, operation 205 is performed to compare thelower one D_(B) of the two center bit count differences with its otherneighbor D_(A).

If D_(B) is less than its other neighbor D_(A), D_(B) is no greater thanits neighbors D_(A) and D_(D). Thus, it can be inferred that a minimalcan be found between the test voltages V_(B) and V_(C). Based on a ratiobetween the differences of D_(B) from its neighbors D_(A) and D_(C), anestimate of the location of the optimized read voltage V_(O) can bedetermined using a technique illustrated in FIG. 5.

If D_(B) is no less than its other neighbor D_(A), it can be assumedthat a minimal can be in the lowest test voltage interval between V_(A)and V_(B). Thus, an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique illustrated in FIG. 6,based on a ratio of the count differences D_(A) and D_(B) that areclosest to the test voltages V_(A) and V_(B).

FIG. 5 illustrates a technique to estimate the location of the optimizedread voltage V_(O) when a center count difference D_(B) is no greaterthan its neighbors D_(A) and D_(C).

Since the count difference D_(B) is the difference of bit counts C_(B)and C_(C) at test voltages V_(B) and V_(C), the location of theoptimized read voltage V_(O) is estimated to be within the voltageinterval or gap between V_(B) and V_(C).

When the increases from the center count difference D_(B) to itsneighbors D_(A) and D_(C) are substantially equal to each other, theoptimized read voltage V_(O) is estimated at the midpoint between V_(B)and V_(C).

The ratio between the increases from the center count difference D_(B)to its neighbors D_(A) and D_(C) can be mapped in a logarithmic scale toa line scale of division between the test voltages V_(B) and V_(C).

For example, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of 1 is mapped to alocation of the optimized read voltage at the midpoint between the testvoltages V_(B) and V_(C).

The ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of 1/2 is mapped to a location ofthe optimized read voltage at the midpoint between the test voltagesV_(B) and V_(C) with an offset of a fixed increment towards V_(B). Forexample, the increment can be one tenth of the voltage gap between V_(B)and V_(C).

Similarly, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of 1/4, 1/8, or 1/16 ismapped to a location of the optimized read voltage at the midpointbetween the test voltages V_(B) and V_(C) with an offset of two, three,or four increments towards V_(B). A ratio (D_(A)−D_(B))/(D_(C)−D_(B))smaller than 1/16 can be mapped to a location of the optimized readvoltage at V_(B).

Similarly, the ratio (D_(C)−D_(B))/(D_(A)−D_(B)) of 1/2, 1/4, 1/8, or1/16 is mapped to a location of the optimized read voltage at themidpoint between the test voltages V_(B) and V_(C) with an offset ofone, two, three, or four increments towards V_(C). A ratio(D_(C)−D_(B))/(D_(A)−D_(B)) smaller than 1/16 can be mapped to alocation of the optimized read voltage at V_(C).

The technique of FIG. 5 can be implemented via setting a coarseestimation of the optimized read voltage at V_(B) (or V_(C)) andadjusting the coarse estimation through applying the increment accordingto comparison of the increase (D_(A)−D_(B)) of the count differenceD_(B) to the count difference D_(A) with fractions or multiples of theincrease (D_(C)−D_(B)) of the count difference D_(B) to the countdifference D_(C). The fractions or multiples of the increase(D_(C)−D_(B)) in a logarithmic scale can be computed through iterativedivision or multiplication by two, which can be implemented efficientlythrough bit-wise left shift or right shift operations.

For example, the initial estimate of the optimized voltage V_(O) can beset at the test voltage V_(B). The increase (D_(A)−D_(B)) can becompared with (D_(C)−D_(B))/16, which can be computed through shiftingthe bits of (D_(C)−D_(B)). If (D_(A)−D_(B)) is greater than(D_(C)−D_(B))/16, the increment of one tenth of the gap between V_(B)and V_(C) can be added to the estimate of the optimized voltage V_(O).Subsequently, (D_(A)−D_(B)) is compared to (D_(C)−D_(B))/8, which can becalculated by shifting the bits of (D_(C)−D_(B))/16. If (D_(A)−D_(B)) isgreater than (D_(C)−D_(B))/8, the same increment of one tenth of the gapbetween V_(B) and V_(C) is further added to the estimation of theoptimized voltage V_(O). Similarly, (D_(A)−D_(B)) is compared to(D_(C)−D_(B))/4, (D_(C)−D_(B))/2, (D_(C)−D_(B)), (D_(C)−D_(B))*2,(D_(C)−D_(B))*4, (D_(C)−D_(B))*8, and (D_(C)−D_(B))*16 one afteranother. If (D_(A)−D_(B)) is greater than any of these scaled versionsof (D_(C)−D_(B)) in a comparison, the same increment is added to theestimate. After the series of comparisons, the resulting estimate can beused as the optimized voltage V_(O).

FIG. 6 illustrates a technique to estimate the location of the optimizedread voltage V_(O) when a side count difference D_(A) is smaller thanits next two count differences D_(B) and D_(C), but one of its neighborshas not been measured (e.g., a count difference between the test voltageV_(A) and a further test voltage that is lower than V_(A)).

Since the count difference D_(A) is the lowest among count differencesD_(A), D_(B) and D_(C), the optimized voltage V_(O) is estimated to bein the test voltage interval gap corresponding to the count differenceD_(A). Since the count difference D_(A) is the difference of bit countsC_(A) and C_(B) at test voltages V_(A) and V_(B), the location of theoptimized read voltage V_(O) is estimated to be within the voltageinterval or gap between V_(A) and V_(B).

In FIG. 6, the location of the optimized read voltage V_(O) within thevoltage interval or gap between V_(A) and V_(B) is based on a ratio ofthe count differences D_(A) and D_(B). The ratio D_(A)/D_(B) in alogarithmic scale is mapped to the linear distribution of the optimizedread voltage V_(O) between V_(A) and V_(B).

For example, the voltage interval or gap between V_(A) and V_(B) can bedivided into five equal increments. The initial estimate of theoptimized voltage V_(O) can be set at the test voltage V_(B). The countdifference D_(A) can be compared to scaled versions of the countdifference D_(B) sequentially, such as D_(B), D_(B)/2, and D_(B)/4. Ifthe count difference D_(A) is smaller than any of the scaled versions ofthe count difference D_(B) in a comparison, the estimate is reduced bythe increment for moving towards the test voltage V_(A).

The curve 157 in FIG. 3 illustrates the change of count difference as afunction of the center of test voltage interval. For example, countdifference D_(A) is at the center of the test voltage interval betweenV_(A) and V_(B); count difference D_(B) is at the center of the testvoltage interval between V_(B) and V_(C); count difference D_(C) is atthe center of the test voltage interval between V_(C) and V_(D); andcount difference D_(D) is at the center of the test voltage intervalbetween V_(D) and V_(E).

The minimum point D_(MIN) on the curve 157 represents a count differencebetween a test voltage interval centered at the optimized read voltageV_(O). The minimum point D_(MIN) can be calculated using the techniqueillustrated in FIGS. 7 to 10.

FIGS. 7 to 10 illustrate a technique to estimate signal and noisecharacteristics centered at an optimized read voltage according to oneembodiment.

When the optimized read voltage V_(O) is determined to be within thecenter test voltage intervals (e.g., V_(B) to V_(C), and V_(C) to V_(D))(e.g., through the comparisons 201 to 205 illustrated in FIG. 4), theoptimized read voltage V_(O) is in a test voltage interval of a countdifference (e.g., D_(B)) that is between its two neighbors (e.g., D_(A)and D_(C)). The minimum point D_(MIN) of such an optimized read voltageV_(O) can be determined using a technique of FIG. 7.

In FIG. 7, D_(MIN) is mapped to a fraction of D_(B) based on the ratioof its differences from its neighbors D_(A) and D_(C). When the ratio(D_(A)−D_(B))/(D_(C)−D_(B)) is less than 1/4 or greater than 4, D_(MIN)is estimated as D_(B)*3/4. When the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) isbetween 1/4 and 4, D_(MIN) is estimated as D_(B).

The count difference D_(MIN) corresponds to a difference between a bitcount measured at a test voltage that is half of the test voltage gapV_(B)−V_(C) below the optimized read voltage V_(O) and a bit countmeasured at a test voltage that is half of the test voltage gapV_(B)−V_(C) above the optimized read voltage V_(O). The count differenceD_(MIN) represents the count of memory cells in the group that havethreshold voltages located between half of the test voltage gapV_(B)−V_(C) below and above the optimized read voltage V_(O).

In some implementations, a count difference D_(MIN2) is a differencebetween a bit count measured at a test voltage that is the full testvoltage gap V_(B)−V_(C) below the optimized read voltage V_(O) and a bitcount measured at a test voltage that is the full test voltage gapV_(B)−V_(C) above the optimized read voltage V_(O). Such a countdifference D_(MIN2) can also be useful in the determination of thequality of data (e.g., the error rate of the hard bit data) read usingthe optimized read voltage V_(O).

FIG. 8 illustrates an example to estimate D_(MIN2) given the ratio of(D_(A)−D_(B))/(D_(C)−D_(B)). When the ratio is between 1/4 and 4,D_(MIN2) is estimated as D_(B)+(D_(A)+D_(C))/4. When the ratio is lessthan 1/4, D_(MIN2) is estimated as D_(B)+D_(A); and when the ratio isgreater than 4, D_(MIN2) is estimated as D_(B) D_(C). Other estimationschemes can also be used to simplify computation while maintaining adesired level of accuracy.

The mapping schemes of FIG. 7 and FIG. 8 can be combined with thetechnique of computing the optimized read voltage V_(O) illustrated inFIG. 5. In FIG. 5, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) is compared to1/16, 1/8, 1/4, 1/2, 1, 2, 4, 8, and 16 to determine the optimized readvoltage V_(O). Based on the comparison results, D_(MIN) can be estimatedas D_(B) or D_(B)*3/4 according to the mapping of FIG. 7; and D_(MIN2)can be estimated as D_(B)+D_(A), D_(B)+(D_(A)+D_(C))/4, or D_(B) D_(C)according to the mapping of FIG. 8.

When the optimized read voltage V_(O) is determined to be within a sidetest voltage interval (e.g., V_(A) to V_(B), or V_(D) to V_(E)) (e.g.,through the comparisons 201 to 205 illustrated in FIG. 4), the optimizedread voltage V_(O) can be estimated as in a test voltage interval of acount difference (e.g., D_(A)) having two closest count differences(e.g., D_(A) and D_(B)) that are located on a same side of the countdifferent (e.g., D_(A)) on voltage axis (e.g., using a techniqueillustrated in FIG. 6). The minimum point D_(MIN) of such an optimizedread voltage V_(O) can be determined using a technique of FIG. 10.

Alternatively, the optimized read voltage V_(O) can be estimated asbeing located at the test voltage (e.g., V_(B)) shared by the two countdifferences. When the optimized read voltage V_(O) is estimated at thetest voltage V_(B), the minimum point D_(MIN) of such an optimized readvoltage V_(O) can be determined using a technique of FIG. 9.

Similar to the determination of the optimized read voltage V_(O) in FIG.6, the determination of D_(MIN) in FIG. 9 or FIG. 10 is based on theratio between the side count difference D_(A) and its neighbor D_(B).

In the example of FIG. 9, the ratio D_(A)/D_(B) is compared to ratios 1,3/4, 1/2, 1/4, 1/8, 1/16, 1/32 respectively. When the ratio D_(A)/D_(B)is between 1 and 3/4, D_(MIN) is estimated to be the same as D_(A). Whenthe ratio D_(A)/D_(B) is decreased down to 1/2, 1/4, 1/8, 1/16, 1/32respectively, the estimate of D_(MIN) is decreased to D_(A)*5/4,D_(A)*6/4, D_(A)*2, D_(A)*11/4, D_(A)*4, D_(A)*6 respectively. Otherestimation schemes can also be used to simplify computation whilemaintaining a desired level of accuracy.

In FIG. 10, the ratio D_(A)/D_(B) is compared to ratio 1/4. When theratio D_(A)/D_(B) is between 1 and 1/4, D_(MIN) is estimated to be thesame as D_(A). When the ratio D_(A)/D_(B) is decreased down up to 1/4 orlower, the estimate of D_(MIN) is decreased to D_(A)*3/4.

In one embodiment, when the optimized read voltage V_(O) is determinedto be within a side test voltage interval (e.g., V_(A) to V_(B)), theestimate of D_(MIN2) for the optimized read voltage V_(O) is the sum ofthe count differences closest to the test interval (e.g., D_(A)+D_(B)).

The techniques of FIGS. 7-10 greatly simplify the calculation of signaland noise characteristics (e.g., D_(MIN) and D_(MIN2)) centered at theoptimized read voltage V_(O).

FIG. 11 shows a method to calculate signal and noise characteristicscentered at an optimized read voltage for reading a group of memorycells according to one embodiment. The method of FIG. 11 can beperformed by processing logic that can include hardware (e.g.,processing device, circuitry, dedicated logic, programmable logic,microcode, hardware of a device, integrated circuit, etc.),software/firmware (e.g., instructions run or executed on a processingdevice), or a combination thereof. In some embodiments, the method ofFIG. 11 is performed at least in part by the controller 115 of FIG. 1,or processing logic in the memory device 130 of FIG. 2. In someimplementations, the computations involved in the method of FIG. 11 aregreatly simplified such that the method can be performed in the memorydevice 130 of FIG. 2. Although shown in a particular sequence or order,unless otherwise specified, the order of the processes can be modified.Thus, the illustrated embodiments should be understood only as examples,and the illustrated processes can be performed in a different order, andsome processes can be performed in parallel. Additionally, one or moreprocesses can be omitted in various embodiments. Thus, not all processesare required in every embodiment. Other process flows are possible.

For example, the method of FIG. 11 can be implemented in a computingsystem of FIG. 1 with a memory device of FIG. 2 and signal noisecharacteristics illustrated in FIG. 3 with some of the operationsillustrated in FIGS. 4-10.

At block 301, a memory device 130 receives a command identifying a groupof memory cells (e.g., 131 or 133) within the memory device 130.Operations at blocks 303 to 307 can be performed in response to thecommand.

At block 303, the memory device 130 measures first signal and noisecharacteristics 139 of the group of memory cells (e.g., 131 or 133)based on first test voltages (e.g., V_(A), V_(B), V_(C), V_(D), andV_(E)).

For example, the first test voltages (e.g., V_(A), V_(B), V_(C), V_(D),and V_(E)) are separated from each other by a predetermined voltage gap(e.g., G=V_(B)−V_(A)=V_(C)−V_(B)=V_(D)−V_(C)=V_(E)−V_(D)). Thus, eachadjacent pair in the first test voltages have the same predeterminedvoltage gap; and the first test voltages are equally spaced according toa predetermined voltage gap.

For example, the calibration circuit 145 of the memory device 130 can beconfigured to measure the first signal and noise characteristics 139 byreading the group of memory cells (e.g., 131 or 133) at the plurality offirst test voltages (e.g., V_(A), V_(B), V_(C), V_(D), and V_(E)). Aftercomputing bit counts (e.g., C_(A), C_(B), C_(C), C_(D), and C_(E)) atthe first test voltages (e.g., V_(A), V_(B), V_(C), V_(D), and V_(E))respectively, count differences (e.g., D_(A), D_(B), D_(C), and D_(D))in the bit counts for pairs of adjacent voltages in the first testvoltages (e.g., V_(A), V_(B), V_(C), V_(D), and V_(E)) are calculated.Each bit count (e.g., D_(A)) at a test voltage (e.g., V_(A)) identifiesa number of memory cells in the group (e.g., 131 or 133) that, when readat the test voltage (e.g., V_(A)), provide a predetermined bit value(e.g., 0 or 1); and each count difference (e.g., D_(A)) of a voltageinterval between a pair of adjacent voltages (e.g., V_(A) and V_(B)) inthe first test voltages (e.g., V_(A), V_(B), V_(C), V_(D), and V_(E)) isa difference between bit counts (e.g., C_(A) and C_(B)) of the pair ofadjacent voltages (e.g., V_(A) and V_(B)).

At block 305, a read manager 113 computes, from the first signal andnoise characteristics 139, an optimized read voltage V_(O) of the groupof memory cells (e.g., 131 or 133).

For example, the optimized read voltage V_(O) can be computed using thetechnique of FIG. 5 based on the ratio between D_(A)−D_(B) andD_(C)−D_(B), or using the technique of FIG. 6 based on the ratio betweenD_(A) and D_(B).

At block 307, the read manager 113 estimates, from the first signal andnoise characteristics 139, second signal and noise characteristics(e.g., D_(MIN) and D_(MIN2)) of the group of memory cells (e.g., 131 or133) that are considered to be based on second test voltages that arecentered at the optimized read voltage V_(O) of the group of memorycells (e.g., 131 or 133). The estimation is performed without actuallyreading the group of memory cells (e.g., 131 or 133) at the second testvoltages.

For example, the second test voltages can include: a first voltage thatis a half of the predetermined voltage gap G above the optimized readvoltage V_(O); a second voltage that is a half of the predeterminedvoltage gap G below the optimized read voltage V_(O); a third voltagethat is the predetermined voltage gap G above the optimized read voltageV_(O); and a fourth voltage that the predetermined voltage gap G belowthe optimized read voltage V_(O). For example, the second signal andnoise characteristics can include a count difference estimate betweenthe bit count at the first voltage (e.g., V_(O)+G/2) and the bit countat the second voltage (e.g., V_(O)−G/2). The second signal and noisecharacteristics can further include a count difference estimate betweenthe bit count at the third voltage (e.g., V_(O)+G) and the bit count atthe fourth voltage (e.g., V_(O)−G).

Typically, at least two of the second test voltages do not coincide withany of the first test voltages (e.g., V_(A), V_(B), V_(C), V_(D), andV_(E)). In some instances, none of the second test voltages coincideswith any of the first test voltages (e.g., V_(A), V_(B), V_(C), V_(D),and V_(E)).

The second signal and noise characteristics (e.g., V_(MIN) and V_(MIN2))can be estimated without reading the group of memory cells at the secondtest voltages.

For example, the second signal and noise characteristics (e.g., D_(MIN)and D_(MIN2)) can be estimated using the techniques of FIG. 7 and FIG. 8based on the ratio between D_(A)−D_(B) and D_(C)−D_(B), or the techniqueof FIG. 9 or FIG. 10 based on the ratio between D_(A) and D_(B).

In one example, when the optimized read voltage V_(O) is determined tobe in a voltage interval (e.g., V_(B) to V_(C)) of a first countdifference (e.g., D_(B)) that is no larger than two second countdifferences (e.g., D_(A) and D_(C)) having voltage intervals (e.g.,V_(A) to V_(B), and V_(C) to V_(D)) that enclose the voltage interval(e.g., V_(B) to V_(C)) of the first count difference (e.g., D_(B)), thememory device 130 is configured to estimate the second signal and noisecharacteristics (e.g., D_(MIN) and D_(MIN2)) based on a ratio betweenincreases (e.g., D_(A)−D_(B), and D_(C)−D_(B)) from the first countdifference (e.g., D_(B)) to the two second count differences (e.g.,D_(A) and D_(C)). For example, the memory device 130 is configured toestimate the second signal and noise characteristics (e.g., D_(MIN) andD_(MIN2)) based on comparison of the ratio (D_(A)−D_(B))/(D_(C)−D_(B))with 1/8, 1/4, 4, and 8, as illustrated in FIGS. 7 and 8. The comparisoncan be performed via compare scaled versions of the increases, such ascomparing (D_(A)−D_(B)) and (D_(C)−D_(B))/8, or comparing(D_(A)−D_(B))*2 and (D_(C)−D_(B))/4.

In another example, when the optimized read voltage V_(O) is determinedto be in a voltage interval (e.g., V_(A) to V_(B)) of a first countdifference (e.g., D_(A)) that is not enclosed within two of the countdifferences in the bit counts for the pairs of the adjacent voltages inthe first test voltages (e.g., V_(A), V_(B), V_(C), V_(D), V_(E)), thememory device 130 is configured to estimate the second signal and noisecharacteristics (e.g., D_(MIN) and D_(MIN2)) based on a ratio betweenthe first count difference (e.g., D_(A)) and a second count difference(e.g., D_(B)) having a voltage interval that is closest, in the countdifferences in the bit counts for the pairs of the adjacent voltages inthe first test voltages, to the voltage interval of the first countdifference (e.g., D_(A)). For example, the memory device is configuredto estimate the second signal and noise characteristics (e.g., D_(MIN)and D_(MIN2)) based on comparison of the ratio with D_(A)/D_(B) with3/4, 1/2, 1/4, 1/8, 1/16, and 1/32, as illustrated in FIGS. 10 and 11.The comparison can be performed via comparing scaled versions of thecount differences, such as comparing D_(A) and D_(B)*3/4, or comparingD_(A)*4 and D_(B)*3.

The scale versions can be generated efficiently through bit-wise shiftoperations that correspond to multiplication to a factor of two to thepower of a number, and/or through addition/accumulation.

The second signal and noise characteristics (e.g., D_(MIN) and D_(MIN2))can be used to calculate an estimate of the bit error count in dataretrieved from the group of memory cells (e.g., 131 and 133), using anempirical formula.

For example, when a group of memory cells are programmed to storemultiple bits of data per memory cell, the group of memory cells can beread at a plurality of read voltages to determine the multiple bitsstored in each memory cells. Reading one bit per cell from the group ofmemory cells includes reading the group of memory cells at some, but notall, read thresholds. Each of the read voltages can be calibrated in away illustrated in FIG. 3 and/or using the techniques of FIGS. 4-6.Subsequently, a count difference D_(MIN) of each calibrated/optimizedread voltage V_(O) can be estimated using the techniques of FIGS. 7-10.The sum S_(DMIN) of the count differences (e.g., D_(MIN)) of thecalibrated/optimized read voltages (e.g., V_(O)) can be used in anempirical formula to calculate the count of bit errors in data read fromthe group of memory cells using the calibrated/optimized read voltages(e.g., V_(O)). For example, the bit error count can be estimated asS_(DMIN)*SF, where the scale factor can be a linear function in the formof e₁*S_(DMIN)+e₂. In one embodiment, e₁=0.0086; and e₂=1.00283.

FIG. 12 shows a method to estimate a bit error count in data retrievedusing a read voltage optimized for reading a group of memory cellsaccording to one embodiment. The method of FIG. 12 can be performed byprocessing logic that can include hardware (e.g., processing device,circuitry, dedicated logic, programmable logic, microcode, hardware of adevice, integrated circuit, etc.), software/firmware (e.g., instructionsrun or executed on a processing device), or a combination thereof. Insome embodiments, the method of FIG. 11 is performed at least in part bythe controller 115 of FIG. 1, or processing logic in the memory device130 of FIG. 2. Although shown in a particular sequence or order, unlessotherwise specified, the order of the processes can be modified. Thus,the illustrated embodiments should be understood only as examples, andthe illustrated processes can be performed in a different order, andsome processes can be performed in parallel. Additionally, one or moreprocesses can be omitted in various embodiments. Thus, not all processesare required in every embodiment. Other process flows are possible.

For example, the method of FIG. 12 can be implemented in a computingsystem of FIG. 2 with a memory device of FIG. 2 and signal noisecharacteristics illustrated in FIG. 3 with some of the operationsillustrated in FIGS. 4-10.

At block 321, a memory device 130 programs a group of memory cells(e.g., 131 or 133) to store a predetermined number of bits per memorycells. Thus, the group of memory cells (e.g., 131 or 133) is be read ata plurality of first voltages to retrieve the predetermine number ofbits per memory cells from the group of memory cells. The optimizedvoltages to read the group of memory cells may change from the pluralityof first voltages over time.

At block 323, a read manager 113 determines a plurality of calibratedread voltages (e.g., V_(O)) corresponding to the plurality of firstvoltages respectively. Each respective calibrated read voltage (e.g.,V_(O)) in the plurality of calibrated read voltages can be calibratedbased on first signal and noise characteristics 139 of the group ofmemory cells (e.g., 131 or 133) measured by reading the group of memorycells at first test voltages (e.g., V_(A), V_(B), V_(C), V_(D), V_(E)),as illustrated in FIG. 3.

At block 325, the read manager 113 computes, from the first signal andnoise characteristics 139, second signal and noise characteristics(e.g., D_(MIN) and/or D_(MIN2)) of the group of memory cells (e.g., 131or 133) at the respective calibrated read voltage (e.g., V_(O)) based onsecond test voltages centered at the respective calibrated read voltage(e.g., V_(O)).

At block 337, the read manager 113 estimates, based on the second signaland noise characteristics (e.g., D_(MIN) and/or D_(MIN2)), a bit errorcount of data retrievable from the group of memory cells (e.g., 131 or133) using the plurality of calibrated read voltages (e.g., V_(O)).

For example, the read manager 113 calculates a sum S_(DMIN) of thesecond signal and noise characteristics (e.g., D_(MIN)) for theplurality of calibrated read voltages (e.g., V_(O)), compute a scalefactor SF from the sum S_(DMIN) according to a predetermined formula(e.g., e₁*S_(DMIN)+e₂), and multiplies the sum S_(DMIN) by the scalefactor SF to generate an estimate of the bit error count. For example,the coefficients e₁ and e₂ can be predetermined from curve fittingmeasured data obtained from similar memory devices.

The method of FIG. 11 can be used to compute the second signal and noisecharacteristics (e.g., D_(MIN)).

For example, the first test voltages are equally spaced according to apredetermined voltage gapG=V_(B)−V_(A)=V_(C)−V_(B)=V_(D)−V_(C)=V_(E)−V_(D). The second testvoltages include: an upper voltage that is a half of the predeterminedvoltage gap above the respective calibrated read voltage (e.g.,V_(O)+G/2), and a lower voltage that is a half of the predeterminedvoltage gap below the respective calibrated read voltage (e.g.,V_(O)−G/2).

For example, the first signal and noise characteristics 139 can includecount differences (e.g., D_(A), D_(B), D_(C), D_(D)) that correspond todifferences of bit counts read at adjacent test voltages. For example,bit counts C_(A) and C_(B) represent the number of memory cells thatprovide a bit value of 0 (or 1) when the group of memory cells (e.g.,131 or 133) is read that the test voltages V_(A) and V_(B). The countdifference D_(A)=C_(B)−C_(A) is calculated for the test voltage intervalfrom V_(A) to V_(B), or for the center (V_(A)+V_(B))/2 of the testvoltage interval. D_(MIN) is the count difference for theoptimized/calibrated read voltage V_(O) for the voltage interval fromV_(O)−G/2 to V_(O)+G/2. As illustrated in FIGS. 7-10, D_(MIN) can becalculated/estimated from (e.g., D_(A), D_(B), D_(C), Do) withoutreading the group of memory cells at the upper voltage V_(O)+G/2 and atthe lower voltage V_(O)−G/2.

The upper voltage V_(O)+G/2 and the lower voltage V_(O)−G/2 do notcoincide with any of the first test voltages (e.g., V_(A), V_(B), V_(C),V_(D), V_(E)), unless V_(O) happens to be at the center of one of thetest intervals (e.g., at (V_(A)+V_(B))/2, (V_(B)+V_(C))/2,(V_(C)+V_(D))/2, or (V_(D)+V_(E))/2).

In general, the ranges of the test voltages for measuring the bit countsto determine the optimize/calibrated read voltage (e.g., V_(O)) do notoverlap with each other. The range of the test voltages (e.g., V_(A) toV_(B)) is in vicinity of the optimized/calibrated read voltage V_(O),configured based on an initial estimate of the location of theoptimized/calibrated read voltage V_(O).

A non-transitory computer storage medium can be used to storeinstructions of the firmware of a memory sub-system (e.g., 110). Whenthe instructions are executed by the controller 115 and/or theprocessing device 117, the instructions cause the controller 115, theprocessing device 117, and/or a separate hardware module to perform themethods discussed above.

FIG. 13 illustrates an example machine of a computer system 400 withinwhich a set of instructions, for causing the machine to perform any oneor more of the methodologies discussed herein, can be executed. In someembodiments, the computer system 400 can correspond to a host system(e.g., the host system 120 of FIG. 1) that includes, is coupled to, orutilizes a memory sub-system (e.g., the memory sub-system 110 of FIG. 1)or can be used to perform the operations of a read manager 113 (e.g., toexecute instructions to perform operations corresponding to the readmanager 113 described with reference to FIGS. 1-12). In alternativeembodiments, the machine can be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, and/or the Internet. Themachine can operate in the capacity of a server or a client machine inclient-server network environment, as a peer machine in a peer-to-peer(or distributed) network environment, or as a server or a client machinein a cloud computing infrastructure or environment.

The machine can be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, a switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computer system 400 includes a processing device 402, a mainmemory 404 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), static random access memory (SRAM), etc.), and a data storagesystem 418, which communicate with each other via a bus 430 (which caninclude multiple buses).

Processing device 402 represents one or more general-purpose processingdevices such as a microprocessor, a central processing unit, or thelike. More particularly, the processing device can be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 402 can also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 402 is configuredto execute instructions 426 for performing the operations and stepsdiscussed herein. The computer system 400 can further include a networkinterface device 408 to communicate over the network 420.

The data storage system 418 can include a machine-readable storagemedium 424 (also known as a computer-readable medium) on which is storedone or more sets of instructions 426 or software embodying any one ormore of the methodologies or functions described herein. Theinstructions 426 can also reside, completely or at least partially,within the main memory 404 and/or within the processing device 402during execution thereof by the computer system 400, the main memory 404and the processing device 402 also constituting machine-readable storagemedia. The machine-readable storage medium 424, data storage system 418,and/or main memory 404 can correspond to the memory sub-system 110 ofFIG. 1.

In one embodiment, the instructions 426 include instructions toimplement functionality corresponding to a read manager 113 (e.g., theread manager 113 described with reference to FIGS. 1-12). While themachine-readable storage medium 424 is shown in an example embodiment tobe a single medium, the term “machine-readable storage medium” should betaken to include a single medium or multiple media that store the one ormore sets of instructions. The term “machine-readable storage medium”shall also be taken to include any medium that is capable of storing orencoding a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “machine-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. The presentdisclosure can refer to the action and processes of a computer system,or similar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus can be specially constructed for theintended purposes, or it can include a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program can be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems can be used with programs in accordance with the teachingsherein, or it can prove convenient to construct a more specializedapparatus to perform the method. The structure for a variety of thesesystems will appear as set forth in the description below. In addition,the present disclosure is not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages can be used to implement the teachings of thedisclosure as described herein.

The present disclosure can be provided as a computer program product, orsoftware, that can include a machine-readable medium having storedthereon instructions, which can be used to program a computer system (orother electronic devices) to perform a process according to the presentdisclosure. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). In someembodiments, a machine-readable (e.g., computer-readable) mediumincludes a machine (e.g., a computer) readable storage medium such as aread only memory (“ROM”), random access memory (“RAM”), magnetic diskstorage media, optical storage media, flash memory components, etc.

In this description, various functions and operations are described asbeing performed by or caused by computer instructions to simplifydescription. However, those skilled in the art will recognize what ismeant by such expressions is that the functions result from execution ofthe computer instructions by one or more controllers or processors, suchas a microprocessor. Alternatively, or in combination, the functions andoperations can be implemented using special purpose circuitry, with orwithout software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific example embodiments thereof. Itwill be evident that various modifications can be made thereto withoutdeparting from the broader spirit and scope of embodiments of thedisclosure as set forth in the following claims. The specification anddrawings are, accordingly, to be regarded in an illustrative senserather than a restrictive sense.

What is claimed is:
 1. A memory device, comprising: an integratedcircuit package enclosing the memory device; and at least one group ofmemory cells formed on at least one integrated circuit die; wherein thememory device is configured to: program the group of memory cells tostore a predetermined number of bits per memory cells, the group ofmemory cells to be read at a plurality of first voltages to retrieve thepredetermined number of bits per memory cells from the group of memorycells; determine a plurality of calibrated read voltages correspondingto the plurality of first voltages respectively, wherein each respectivecalibrated read voltage in the plurality of calibrated read voltages iscalibrated based on first signal and noise characteristics of the groupof memory cells measured by reading the group of memory cells at firsttest voltages; compute, from the first signal and noise characteristics,second signal and noise characteristics of the group of memory cells atthe respective calibrated read voltage based on second test voltages;and estimate, based on the second signal and noise characteristics, abit error count of data retrievable from the group of memory cells usingthe plurality of calibrated read voltages.
 2. The memory device of claim1, further configured to: calculate a sum of the second signal and noisecharacteristics for the plurality of calibrated read voltages; andmultiply the sum by a scale factor to generate an estimate of the biterror count.
 3. The memory device of claim 2, further configured to:compute the scale factor from the sum according to a predeterminedformula.
 4. The memory device of claim 3, wherein the scale factor is alinear function of the sum, the linear function having predeterminedcoefficients.
 5. The memory device of claim 4, wherein the first testvoltages are equally spaced according to a predetermined voltage gap. 6.The memory device of claim 5, wherein to measure the first signal andnoise characteristics, the memory device is configured to: read thegroup of memory cells at the plurality of first test voltages; determinebit counts at the first test voltages respectively, wherein each bitcount at a test voltage identifies a number of memory cells in the groupthat, when read at the test voltage, provide a predetermined bit value;and compute count differences in the bit counts for pairs of adjacentvoltages in the first test voltages, wherein each count difference of avoltage interval between a pair of adjacent voltages in the first testvoltages is a difference between bit counts of the pair of adjacentvoltages.
 7. The memory device of claim 6, wherein the second testvoltages include: an upper voltage that is a half of the predeterminedvoltage gap above the respective calibrated read voltage, and a lowervoltage that is a half of the predetermined voltage gap below therespective calibrated read voltage.
 8. The memory device of claim 7,wherein the second signal and noise characteristics include a countdifference between a bit count at the upper voltage and a bit count atthe lower voltage.
 9. The memory device of claim 8, wherein the secondsignal and noise characteristics are determined without reading thegroup of memory cells at the upper voltage and at the lower voltage. 10.The memory device of claim 9, wherein at least two of the second testvoltages do not coincide with any of the first test voltages.
 11. Thememory device of claim 10, wherein ranges of test voltages used incalibration of the plurality of calibrated read voltages do not overlapwith each other.
 12. A method, comprising: programming a group of memorycells to store a predetermined number of bits per memory cells, thegroup of memory cells to be read at a plurality of first voltages toretrieve the predetermined number of bits per memory cells from thegroup of memory cells; determining a plurality of calibrated readvoltages corresponding to the plurality of first voltages respectively,wherein each respective calibrated read voltage in the plurality ofcalibrated read voltages is calibrated based on first signal and noisecharacteristics of the group of memory cells measured by reading thegroup of memory cells at first test voltages; computing, from the firstsignal and noise characteristics, second signal and noisecharacteristics of the group of memory cells at the respectivecalibrated read voltage based on second test voltages; and estimating,based on the second signal and noise characteristics, a bit error countof data retrievable from the group of memory cells using the pluralityof calibrated read voltages.
 13. The method of claim 12, furthercomprising: calculating a sum of the second signal and noisecharacteristics for the plurality of calibrated read voltages; andmultiplying the sum by a scale factor to generate an estimate of the biterror count.
 14. The method of claim 13, further comprising: computingthe scale factor from the sum according to a predetermined formula;wherein the scale factor is a linear function of the sum, the linearfunction having predetermined coefficients.
 15. The method of claim 14,wherein the first test voltages are separated from each other by apredetermined voltage gap; and the method further comprises: measuringof the first signal and noise characteristics by: reading the group ofmemory cells at the plurality of first test voltages; determining bitcounts at the first test voltages respectively, wherein each bit countat a test voltage identifies a number of memory cells in the group that,when read at the test voltage, provide a predetermined bit value; andcomputing count differences in the bit counts for pairs of adjacentvoltages in the first test voltages, wherein each count difference of avoltage interval between a pair of adjacent voltages in the first testvoltages is a difference between bit counts of the pair of adjacentvoltages.
 16. The method of claim 15, wherein the second test voltagesinclude: an upper voltage that is a half of the predetermined voltagegap above the respective calibrated read voltage, and a lower voltagethat is a half of the predetermined voltage gap below the respectivecalibrated read voltage.
 17. The method of claim 16, wherein the secondsignal and noise characteristics include a count difference between abit count at the upper voltage and a bit count at the lower voltage. 18.A memory sub-system, comprising: a processing device; and at least onememory device, the memory device having a group of memory cells formedon an integrated circuit die, the group of memory cells programmed tostore a predetermined number of bits per memory cells, the group ofmemory cells to be read at a plurality of first voltages to retrieve thepredetermined number of bits per memory cells from the group of memorycells; wherein the processing device is configured to transmit, to thememory device, a command with an address identifying the group of memorycells; and wherein in response to the command, the memory device isconfigured to: determine a plurality of calibrated read voltagescorresponding to the plurality of first voltages respectively, whereineach respective calibrated read voltage in the plurality of calibratedread voltages is calibrated based on first signal and noisecharacteristics of the group of memory cells measured by reading thegroup of memory cells at first test voltages; compute, from the firstsignal and noise characteristics, second signal and noisecharacteristics of the group of memory cells at the respectivecalibrated read voltage based on second test voltages; and estimate,based on the second signal and noise characteristics, a bit error countof data retrievable from the group of memory cells using the pluralityof calibrated read voltages.
 19. The memory sub-system of claim 18,wherein the memory device is further configured to: calculate a sum ofthe second signal and noise characteristics for the plurality ofcalibrated read voltages; compute a scale factor from the sum accordingto a predetermined formula; multiply the sum by the scale factor togenerate an estimate of the bit error count; wherein adjacent pairs ofthe first test voltages are separated from each other by a predeterminedvoltage gap; and wherein to measure the first signal and noisecharacteristics, the memory device is configured to: read the group ofmemory cells at the plurality of first test voltages; determine bitcounts at the first test voltages respectively, wherein each bit countat a test voltage identifies a number of memory cells in the group that,when read at the test voltage, provide a predetermined bit value; andcompute count differences in the bit counts for pairs of adjacentvoltages in the first test voltages, wherein each count difference of avoltage interval between a pair of adjacent voltages in the first testvoltages is a difference between bit counts of the pair of adjacentvoltages.
 20. The memory sub-system of claim 19, wherein the second testvoltages include: an upper voltage that is a half of the predeterminedvoltage gap above the optimized read voltage; and a lower voltage thatis a half of the predetermined voltage gap below the optimized readvoltage; wherein none of the second test voltages is equal to any of thefirst test voltages; and wherein the second signal and noisecharacteristics include a count difference between a bit count at theupper voltage and a bit count at the lower voltage.